Predicting the Heat of Hydride Formation by Graph Neural Network ‐ Exploring the Structure–Property Relation for Metal Hydrides-Reference-Cited by-同舟云学术

Predicting the Heat of Hydride Formation by Graph Neural Network ‐ Exploring the Structure–Property Relation for Metal Hydrides

Published:2022-07 Issue:9 Volume:5 Page:2200293
ISSN:2513-0390
Container-title:Advanced Theory and Simulations
language:en
Short-container-title:Advcd Theory and Sims

Author:

Batalović Katarina¹^ORCID,Radaković Jana¹,Paskaš Mamula Bojana¹,Kuzmanović Bojana¹,Medić Ilić Mirjana¹

Affiliation:

1. Center of Excellence for Hydrogen and Renewable Energy VINCA Institute of Nuclear Sciences – National Institute of the Republic of Serbia University of Belgrade P.O.Box 522 Belgrade 11000 Serbia

Funder

Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja

Publisher

Wiley

Subject

Multidisciplinary,Modeling and Simulation,Numerical Analysis,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adts.202200293

Reference47 articles.

1. A review on the current progress of metal hydrides material for solid-state hydrogen storage applications

2. Niche applications of metal hydrides and related thermal management issues

3. Metal Hydrides and Related Materials. Energy Carriers for Novel Hydrogen and Electrochemical Storage

4. Future perspectives of thermal energy storage with metal hydrides

5. Tailoring magnesium based materials for hydrogen storage through synthesis: Current state of the art

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